{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe2d16c6-5198-4ece-a711-90155e9a626f",
   "metadata": {},
   "source": [
    "Chapter 20\n",
    "\n",
    "# K均值聚类\n",
    "Book_7《机器学习》 | 鸢尾花书：从加减乘除到机器学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b54d229-e507-43b1-bb1d-4494a71141c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! pip install --upgrade threadpoolctl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d16e52f2-a5fd-49e0-abf0-3638f9cf19ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn import datasets\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20a77499-eab5-4f36-bbf5-655a4f4a7276",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create color maps\n",
    "rgb = [[255, 238, 255],  # red\n",
    "       [219, 238, 244],  # blue\n",
    "       [228, 228, 228]]  # black\n",
    "rgb = np.array(rgb)/255.\n",
    "\n",
    "cmap_light = ListedColormap(rgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "92499ea2-bc28-4e8b-b2eb-2c6eb9e6cc0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入鸢尾花数据\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d7fba08-6cc7-44a1-93cf-4dcff56a24db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only use the first two features: sepal length, sepal width\n",
    "# 取出鸢尾花前两个特征\n",
    "X_train = iris.data[:, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92b040f4-340d-4058-8fb9-8e28fa5e31c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "938ace85-57c2-441a-b61f-abbdee0555a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vector of labels\n",
    "y_train = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "84b45536-10b5-420d-82c8-cdef4d5c0726",
   "metadata": {},
   "outputs": [],
   "source": [
    "# KMeans(n_clusters=2, random_state=0, n_init=\"auto\").fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "12150a3f-f487-4cef-98f8-546c9a59d35c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建KMeans对象\n",
    "kmeans = KMeans(n_clusters=3, n_init = 'auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0f306e4-4eb6-45ea-bc4b-841fac229e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, n_init=&#x27;auto&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3, n_init=&#x27;auto&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3, n_init='auto')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用KMeans算法训练数据\n",
    "kmeans.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "af12c41a-3188-46a8-9a0e-9bddb873368b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成网格数据\n",
    "plot_step = 0.02\n",
    "xx, yy = np.meshgrid(np.linspace(4, 8, int(4/plot_step + 1)),\n",
    "                     np.linspace(1.5, 4.5, int(3/plot_step + 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1ed17e7a-cfdc-4036-911d-d335fb6645f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用KMeans模型对网格中的点进行预测\n",
    "# 并将预测结果整形成与网格相同形状的矩阵\n",
    "Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff5bc15a-af7b-4e41-a1b9-aafacbe4351a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# plot regions\n",
    "plt.contourf(xx, yy, Z, cmap=cmap_light)\n",
    "\n",
    "# plot sample data\n",
    "plt.scatter(x=X_train[:, 0], y=X_train[:, 1], color=np.array([0, 68, 138])/255., alpha=1.0, \n",
    "                linewidth = 1, edgecolor=[1,1,1])\n",
    "\n",
    "# plot decision boundaries\n",
    "plt.contour(xx, yy, Z, levels=[0,1,2], colors=np.array([0, 68, 138])/255.)\n",
    "\n",
    "# plot centroids\n",
    "centroids = kmeans.cluster_centers_\n",
    "\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1], marker=\"x\", s=100, linewidths=1.5,\n",
    "            color=\"k\")\n",
    "\n",
    "ax.set_xticks(np.arange(4, 8.5, 0.5))\n",
    "ax.set_yticks(np.arange(1.5, 5, 0.5))\n",
    "ax.set_xlim(4, 8)\n",
    "ax.set_ylim(1.5, 4.5)\n",
    "plt.xlabel(iris.feature_names[0])\n",
    "plt.ylabel(iris.feature_names[1])\n",
    "ax.grid(linestyle='--', linewidth=0.25, color=[0.5,0.5,0.5])\n",
    "ax.set_aspect('equal')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
